Method and device for fine evaluation of carbon footprint of lithium battery supply chain based on LIB-CCGE model and medium

Abstract

The present application relates to the technical fields of environmental science, energy economics and sustainable development, and discloses a lithium battery supply chain carbon footprint fine evaluation method and device based on a LIB-CCGE model and a medium, wherein the method comprises: acquiring multi-source data and fusing the multi-source data into multi-source heterogeneous data; dividing the lithium battery supply chain into multiple stages and establishing material balance equations and energy consumption and carbon emission equations for each stage; based on the multi-source heterogeneous data, establishing a LIB-CCGE model for simulating the influence of energy structure, production technology efficiency and / or environmental policy in different regions on carbon footprint and calculating carbon emissions of the lithium battery supply chain at different stages. Through the integration of multi-source data, the refinement of the life cycle stage and the introduction of regional economic dynamic parameters, the present application is suitable for the carbon emission accounting, prediction and optimization of the whole life cycle of lithium batteries, and provides technical support for low-carbon supply chain management.

Description

Technical Field

[0001] This invention relates to the fields of environmental science, energy economics and sustainable development technology, and more specifically, to a method, apparatus and medium for refined assessment of the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model. Background Technology

[0002] Lithium-ion batteries (LIBs), with their advantages of high energy density, long cycle life, and low self-discharge rate, have become the primary energy storage technology in electric vehicles, energy storage systems, and consumer electronics. With increasing global emphasis on clean energy and a low-carbon economy, the demand for LIBs is experiencing explosive growth. However, the rapid expansion of the LIB supply chain has also brought significant environmental impacts, particularly carbon emissions, which are receiving increasing attention. Therefore, accurately assessing the carbon footprint of the LIB supply chain and developing effective emission reduction strategies are crucial for achieving sustainable development in the LIB industry.

[0003] Currently, carbon footprint assessment of the lithium battery supply chain mainly relies on two methods: traditional life cycle assessment (LCA) and static input-output (IO) models. While some research and practice have been conducted, certain shortcomings remain: Traditional LCA (Limited Carbon Assessment) methods, based on ISO 14040 / 14044 standards, estimate carbon emissions from raw material extraction, manufacturing, and use (e.g., electric vehicle operation) to recycling (e.g., material reuse) by collecting self-reported data from enterprises, industry averages, or public databases. However, this method has several shortcomings in practical applications. First, LCA typically relies on static data and fixed assumptions (e.g., emission factors or energy structure at a specific point in time), making it difficult to reflect the dynamic impact of technological advancements (e.g., increased battery energy density), market changes (e.g., lithium price fluctuations), or policy adjustments (e.g., carbon tax implementation) on the carbon footprint, thus limiting the timeliness and accuracy of the assessment results. Furthermore, LCA's estimation of implicit emissions from upstream (e.g., raw material transportation) and downstream (e.g., recycling processes) of the supply chain is relatively crude, and the data source is singular and lacks integration of multi-source heterogeneous data, leading to assessment errors of up to 20%-30% in complex scenarios.

[0004] Static IO models, based on national or industry input-output tables, calculate implicit emissions in the supply chain using the Leontief inverse matrix. This method can reflect the indirect impacts between economic activities, such as the contribution of raw material extraction to carbon emissions in the energy sector. However, its static nature makes it unsuitable for the dynamic demands of the rapidly developing lithium-ion battery industry, such as advancements in battery recycling technology (e.g., increasing hydrometallurgical recovery rates from 70% to 90%) or energy structure optimization (e.g., increasing the proportion of renewable energy). Furthermore, IO models have limited ability to model the segmented stages of the lithium-ion battery supply chain; for example, they cannot accurately distinguish the carbon emission contributions of cathode and anode materials in battery manufacturing, nor can they quantify the emission reduction potential of the recycling stage. This results in overly general assessments and a lack of practical guidance for specific optimization measures. Additionally, existing IO models have long data update cycles (typically 5-10 years), making it difficult to reflect the real-time state of the industry.

[0005] In summary, existing technologies have significant limitations in terms of lifecycle coverage, regional adaptability, and assessment accuracy, making it difficult to meet the refined assessment needs of the lithium battery industry's low-carbon development. Summary of the Invention

[0006] This invention proposes a refined assessment method, device, and medium for the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model. This overcomes the limitations of traditional LCA and static IO models, providing a more accurate, dynamic, and regionally adaptable carbon emission assessment tool, and providing a scientific basis for the low-carbon development and policy formulation of the lithium battery industry.

[0007] Specifically, traditional LCA methods rely on static data and fixed assumptions, making it difficult to adapt to the dynamic impacts of technological advancements, market changes, and policy adjustments. Static IO models, due to their static nature and lagging data updates, cannot reflect the real-time state of the lithium battery supply chain. Furthermore, both methods are insufficient in handling regional differences and the carbon emission contributions of different segments of the supply chain, resulting in assessment results lacking accuracy and practicality. To address these issues, the objectives of this invention include the following aspects: First, by introducing a dynamically computable general equilibrium (CGE) model and combining it with real-time updated multi-source data, a dynamic assessment of the carbon footprint of the lithium battery supply chain is achieved. This overcomes the timeliness limitations of traditional methods and ensures that the assessment results reflect the impact of technological advancements (such as improved battery recycling rates) and market fluctuations (such as changes in raw material prices). Second, by incorporating region-specific economic and environmental parameters, the problem of existing technologies failing to accurately reflect regional differences is addressed. For example, the carbon footprint differences under different energy structures (such as hydropower-dominated vs. thermal power-dominated) provide support for regionalized low-carbon strategies. Finally, by refining the material balance and energy flow modeling of each link in the supply chain (such as raw material mining, cathode material processing, and recycling), the accuracy of the assessment is improved, compensating for the shortcomings of existing methods in estimating implicit emissions and calculating detailed links, and controlling the assessment error to below 15%.

[0008] In summary, this invention aims to provide a comprehensive, accurate, and practical carbon footprint assessment method for the lithium battery supply chain through dynamic, regionally adaptable, and refined technological improvements. This will support enterprises in optimizing supply chain management and governments in formulating scientific emission reduction policies, thereby promoting the sustainable development of the lithium battery industry.

[0009] According to a first aspect of the present invention, a method for refined assessment of the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model is provided, the method comprising: Acquire multi-source data and merge the multi-source data into multi-source heterogeneous data; wherein, the multi-source data includes enterprise production data, industry statistics data, public databases and regional economic data related to the lithium battery supply chain; The lithium battery supply chain is divided into multiple stages, and material balance equations and energy consumption and carbon emission equations are established for each stage. Based on the aforementioned multi-source heterogeneous data, a LIB-CCGE model is established; wherein, the LIB-CCGE model is used to simulate the impact of energy structure, production technology efficiency and / or environmental policies on carbon footprint in different regions; Based on the LIB-CCGE model, carbon emissions at different stages of the lithium battery supply chain are calculated.

[0010] Furthermore, the enterprise production data includes energy consumption in battery manufacturing and energy consumption per unit output of cathode material LiCoO2; the industry statistics include global lithium production; the public databases include IEA energy emission factors and coal-fired power; and the regional economic data includes the proportion of thermal power in the region.

[0011] Furthermore, the multi-source data is fused into multi-source heterogeneous data using the following formula: ; in, For the merged dataset, For the data from the i-th data source, As a data fusion function, it adopts ontology-based mapping rules and reasoning mechanisms to clean, standardize and preprocess the collected raw data, including removing duplicate and erroneous data, handling missing and outlier values, converting data of different dimensions and units into a unified format, and storing the processed data in the database.

[0012] Furthermore, the lithium battery supply chain is divided into five stages: raw material mining, material processing, battery manufacturing, usage, and recycling and reuse; these stages are represented in the model as sectors.

[0013] Furthermore, the material balance equation is expressed as: M in,r,s = M out, r,s + M loss, r,s in, M in,r,s This represents the material input quantity for department s within region r. M out,r,s This represents the material output of department s in region r. M loss,r,s This represents the material loss amount in department s within region r; The energy consumption and carbon emission equation is expressed as follows: ; ; ; in, It is the greenhouse gas em emitted by sector s in region r. It is the emission factor of greenhouse gas em from fuel f in sector s of region r. It represents the amount of fuel f consumed by department s in region r. Em is the conversion factor for converting a unit of greenhouse gas em to a carbon dioxide equivalent with uniform dimensions. EmTot is the total greenhouse gas emissions from sector s using fuel f in region r after dimension unification. EF f,r,s The greenhouse gas normalized emission factor for fuel f in sector r of region r.

[0014] Furthermore, the core equations of the LIB-CCGE model include: The production function is expressed as: ; in, For the total output of sector s in region r, Generate function scaling parameters for CES. and These represent the capital and labor factor inputs of sector s in region r, respectively. This represents the production factor ratio parameter of the CES production function. Represents the elasticity of substitution coefficient for the CES production function; The consumption function is expressed as: ; in, For the consumption of products by sector s in region r, It's a consumer preference. It represents the disposable income of region r; The investment function is expressed as: ; in, For investment in regional products, It is an investment tendency. It is the total capital stock of region r; The trade function is expressed as: ; in, This represents the quantity of products from sector s imported by region r from region t. Indicates the trade share parameter. This represents the production price of product in department s of region t. This represents the price of products from sector s imported from region t by region r. Indicates the elasticity of substitution. This represents the total demand of region r for products from department s.

[0015] Furthermore, based on the LIB-CCGE model, carbon emissions at different stages of the lithium battery supply chain are calculated, including direct emissions, indirect emissions, and implicit emissions.

[0016] Furthermore, the calculation formula for the direct emissions is as follows: ; in, E direct For direct emissions, EF f,r,s For the emission factor of fuel f in sector r of region, F f,r,s The consumption of primary energy f by sector s in region r. E process,r,s It refers to the process emissions of department s in region r; The formula for calculating the indirect emissions is as follows: ; in, E indirect Indirect emissions, EF g,r,s The emission factor for the purchase of secondary energy by the regional r sector g. G g,r,s The emissions of secondary energy purchased from external sources by the regional r sector; The formula for calculating the implicit emissions is as follows: ; in, E embodied This represents the implicit emission vectors for products from each department. Let A be the Leontief inverse matrix, and let A be the direct consumption coefficient matrix obtained from the input-output table.

[0017] According to a second aspect of the present invention, a refined assessment device for the carbon footprint of a lithium battery supply chain based on the LIB-CCGE model is provided, the device comprising: The data acquisition module is configured to acquire multi-source data and merge the multi-source data into multi-source heterogeneous data; wherein, the multi-source data includes enterprise production data, industry statistics data, public databases and regional economic data related to the lithium battery supply chain; The phase modeling module is configured to divide the lithium battery supply chain into multiple phases and establish material balance equations and energy consumption and carbon emission equations for each phase. The model building module is configured to build a LIB-CCGE model based on the multi-source heterogeneous data; wherein the LIB-CCGE model is used to simulate the impact of energy structure, production technology efficiency and / or environmental policies on carbon footprint in different regions; The carbon emission calculation module is configured to calculate the carbon emissions of the lithium battery supply chain at different stages based on the LIB-CCGE model.

[0018] According to a third aspect of the present invention, a readable storage medium is provided, the readable storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the method described above.

[0019] The present invention has at least the following beneficial effects: The technical solution of this invention, through a refined assessment method for the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model, significantly improves the dynamism, regional adaptability, and accuracy of the assessment compared to traditional Life Cycle Assessment (LCA) and Static Input-Output (IO) models, providing more scientific and practical technical support for the low-carbon development and policy formulation of the lithium battery industry. The advantages and positive effects are elaborated in detail below from three aspects: the improvement principle, the implementation process, and the specific effects.

[0020] First, this invention overcomes the shortcomings of traditional methods in terms of dynamism by introducing a dynamic CGE model and multi-source data fusion. Traditional LCA relies on static data and fixed assumptions (such as an emission factor of 0.8 kg CO2 / kWh at a certain point in time), which cannot reflect the impact of technological progress (such as an increase in battery recycling rate from 70% to 90%) or market changes (such as a 20% fluctuation in lithium prices); static IO models also lack timeliness due to their long data update cycle (usually 5-10 years). This solution uses real-time updated multi-source data in step 1 (such as adjusting the IEA emission factor annually), and simulates technological progress and changes in economic variables through a dynamic CGE model in step 3. For example, if a battery company in a certain region introduces a new recycling technology in 2023, the LIB-CCGE model estimates that the carbon emission reduction potential of the recycling process increases from 8% to 15%, while the traditional LCA still estimates 8% based on old data, with an error of 7%. This improvement makes the assessment results closer to the current state of the industry, significantly enhancing its dynamism.

[0021] Secondly, this invention enhances regional adaptability by employing region-specific parameters and multi-regional CGE modeling, addressing the problem that traditional methods cannot accurately reflect regional differences. Traditional LCA and IO models typically use a uniform emission factor (e.g., the national average electricity emission factor of 0.8 kg CO2 / kWh), ignoring regional differences in energy structure (e.g., the carbon footprint of a region with 60% hydropower differs by 30% from that of a region with 70% thermal power). For example, in a region dominated by hydropower, the model estimates the carbon footprint of battery manufacturing at 10 kg CO2e / kWh, while in a region dominated by thermal power, it estimates 13 kg CO2e / kWh, with an error of only 2%-3% compared to measured values ​​(10.2 kg and 13.5 kg), far superior to the 15% error of LCA. This regionalization improvement provides targeted support for low-carbon strategies in different regions.

[0022] Furthermore, this invention improves the accuracy and comprehensiveness of the assessment through full lifecycle segmentation and refined modeling, compensating for the shortcomings of traditional methods in supply chain accounting. Traditional LCA provides coarse estimates of implicit emissions (such as raw material transportation), and IO models lack segmented modeling (such as the difference between cathode materials and recycling). This solution subdivides the process into five stages in step 2 (raw material mining, material processing, battery manufacturing, use, and recycling) and establishes material balance and energy flow models; in step 4, it covers the entire supply chain through precise calculations of direct, indirect, and implicit emissions. For example, the measured carbon footprint of a certain region is 12.8 kg CO2e / kWh, the LIB-CCGE model estimates it as 12.5 kg CO2e / kWh (error of 2.3%), while the LCA estimates it as 15 kg CO2e / kWh (error of 17%). The specific process is as follows: the material balance model quantifies the loss (e.g., mining loss of 100 kg), the energy flow model calculates energy consumption and emissions (e.g., 1800 kg CO2), and the implicit emissions are accurately estimated through the Leontief inverse matrix (e.g., 500 kg CO2e), thereby controlling the error to below 15% and improving the accuracy by 20%-25%.

[0023] Furthermore, the positive effects of this invention are also reflected in practical applications. By outputting carbon footprint data for each stage and region, companies can identify high-carbon processes and optimize their supply chains. For example, a company that adjusts its raw material procurement to hydropower regions based on model results reduces its carbon footprint by 10%; after optimizing its recycling process, its emission reduction potential increases to 15%, resulting in a total reduction of 12%-20% in carbon emissions. Governments can also formulate policies based on regionalized results, such as promoting clean energy in thermal power regions, which is expected to achieve emission reductions of 8%-10%. These effects stem from the synergistic effect of dynamic data, regional modeling, and refined accounting, providing a scientific basis for the low-carbon transformation of the lithium battery industry.

[0024] In summary, the technical solution presented in this application significantly improves the accuracy, comprehensiveness, and practicality of carbon footprint assessment through dynamic, regionally adaptable, and refined improvements. Compared to traditional LCA and IO models, its error is reduced from 20%-30% to below 15%, dynamically reflecting technological advancements and market changes, providing regional support for policy formulation, and offering refined guidance for enterprise optimization, demonstrating significant technological advantages and application value. Attached Figure Description

[0025] Figure 1 A flowchart of a refined assessment method for the carbon footprint of a lithium battery supply chain based on the LIB-CCGE model according to an embodiment of the present invention is shown. Figure 2 A structural diagram of a refined assessment device for the carbon footprint of a lithium battery supply chain based on the LIB-CCGE model, according to an embodiment of the present invention, is shown. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and specific examples, but this is not intended to limit the present invention. If there is no necessary sequential relationship between the various steps described herein, the order in which they are described as examples should not be considered a limitation. Those skilled in the art should understand that the order can be adjusted, as long as it does not disrupt the logical consistency between them and render the entire process impossible.

[0027] This invention provides a refined assessment method for the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model. Through a dynamically computable general equilibrium (CGE) model, multi-source data integration, and full lifecycle segmentation modeling, it achieves dynamic, regionalized, and high-precision accounting of carbon emissions from the lithium battery supply chain. Figure 1 As shown, the method is implemented through the following steps S10-S40.

[0028] S10: Acquire multi-source data and merge the multi-source data into multi-source heterogeneous data; wherein, the multi-source data includes enterprise production data, industry statistics data, public databases and regional economic data related to the lithium battery supply chain.

[0029] Step S10 provides comprehensive and dynamic input data for the LIB-CCGE model. Data sources include enterprise production data (such as energy consumption in battery manufacturing, with a unit production energy consumption of 15 MJ / kg for LiCoO2 cathode material), industry statistics (such as global lithium production of 1.3 million tons in 2023), public databases (such as the IEA energy emission factor, with coal-fired power at 0.9 kg CO2 / kWh), and regional economic data (such as the 70% thermal power share in a certain region in the GTAP database). To ensure data consistency, an ontology mapping method is used to fuse multi-source heterogeneous data. The fusion process includes data cleaning (removing duplicate values), standardization (unifying units, such as converting MJ to kWh), and dynamic updates (adjusting the emission factor annually based on the IEA report). For example, the 2023 production data (energy consumption of 12 MJ / kg) and the IEA emission factor (0.8 kg CO2 / kWh) of a battery company are fused to generate a unified input dataset, providing a foundation for subsequent modeling.

[0030] In some embodiments, the multi-source data is fused into multi-source heterogeneous data using the following formula: ; in, For the merged dataset, For the data from the i-th data source, As a data fusion function, it adopts ontology-based mapping rules and reasoning mechanisms to clean, standardize and preprocess the collected raw data, including removing duplicate and erroneous data, handling missing and outlier values, converting data of different dimensions and units into a unified format, and storing the processed data in the database.

[0031] S20: Divide the lithium battery supply chain into multiple stages and establish material balance equations and energy consumption and carbon emission equations for each stage.

[0032] Step S20 breaks down the lithium battery supply chain into five stages and establishes material balance and energy flow models for each stage to achieve a refined assessment of the entire lifecycle. The five stages are: raw material mining (e.g., lithium ore, cobalt ore), material processing (e.g., cathode materials, graphite anode materials), battery manufacturing, usage stage (e.g., electric vehicle operation), and recycling and reuse. By quantifying the material and energy flows at each stage, the traceability of the assessment is ensured. Example: In the raw material mining stage, 1000 kg of lithium ore is input, 900 kg of lithium metal is produced, 100 kg is lost, and energy consumption is 2000 kWh (thermal power, emission factor 0.9 kg CO2 / kWh), resulting in an energy consumption emission of 1800 kg CO2. This process covers the entire supply chain, laying the foundation for subsequent accounting.

[0033] In some embodiments, the material balance equation is expressed as: M in,r,s = M out, r,s + M loss, r,s； in, M in,r,s This represents the material input quantity of department s in region r, in kg (e.g., 1000 kg of lithium ore). M out,r,s This represents the material output of department s in region r, in kg (e.g., 900 kg of lithium metal). M loss,r,s This represents the material loss of department s in region r, in kg (e.g., 100 kg for mining loss).

[0034] The energy consumption and carbon emission equation is expressed as follows: ; ; ; in, It is the greenhouse gas em emitted by sector s in region r. It is the emission factor of greenhouse gas em from fuel f in sector s of region r. It represents the amount of fuel f consumed by department s in region r. Em is the conversion factor for converting a unit of greenhouse gas em to a carbon dioxide equivalent with uniform dimensions. EmTot is the total greenhouse gas emissions from sector s in region r using fuel f after dimension unification. EF f,r,s The greenhouse gas normalized emission factor for fuel f in sector r of region r.

[0035] S30: Based on the aforementioned multi-source heterogeneous data, establish a LIB-CCGE model; wherein, the LIB-CCGE model is used to simulate the impact of energy structure, production technology efficiency and / or environmental policies on carbon footprint in different regions.

[0036] Step S30 enhances the regional adaptability of the LIB-CCGE model by introducing region-specific parameters and a dynamic CGE (Computable General Equilibrium) model, enabling it to reflect the impact of different regions' energy structures, production technology efficiency, and environmental policies on carbon footprint. Parameters include energy structure (e.g., hydropower accounting for 60% of a region's output), production technology efficiency (e.g., electrolytic aluminum energy consumption of 12 MJ / kg), and environmental policies (e.g., a carbon tax of $20 per ton). Based on the GTAP database, a multi-regional CGE model is constructed to simulate inter-regional production, consumption, and trade behaviors. The CGE model consists of five modules: production, consumption, trade, energy and emissions, and policy constraints. These modules are interconnected through data flow and mathematical relationships to jointly model regional economic dynamics. A working example: A region imports lithium metal at a price ratio of 1.2 and a substitution elasticity of 2.0, calculating an import volume of 500 kg to dynamically reflect the impact of trade on carbon footprint; while in hydropower-dominated regions, energy structure adjustments reduce carbon emissions by 20%. This design allows the model to adapt to different regional economic and environmental conditions, providing support for regionalized carbon footprint assessment.

[0037] In some embodiments, the core equations of the LIB-CCGE model include: The production function is expressed as: ; in, For the total output of sector s in region r, Generate function scaling parameters for CES. and These represent the capital and labor factor inputs of sector s in region r, respectively. This represents the production factor ratio parameter of the CES production function. Represents the elasticity of substitution coefficient for the CES production function; The consumption function is expressed as: ; in, For the consumption of products by sector s in region r, It's a consumer preference. It represents the disposable income of region r; The investment function is expressed as: ; in, For investment in regional products, It is an investment tendency. It is the total capital stock of region r; The trade function is expressed as: ; in, This represents the quantity of products from sector s imported by region r from region t. Indicates the trade share parameter. This represents the production price of product in department s of region t. This represents the price of products from sector s imported from region t by region r. Indicates the elasticity of substitution. This represents the total demand of region r for products from department s.

[0038] S40: Based on the LIB-CCGE model, calculate the carbon emissions of the lithium battery supply chain at different stages.

[0039] Step S40 calculates direct, indirect, and implicit emissions using the LIB-CCGE model to achieve a comprehensive assessment of the carbon footprint. Direct emissions are calculated based on fuel consumption and process emissions, indirect emissions are based on waste emissions (such as electricity consumption), and implicit emissions are estimated through indirect impacts upstream in the supply chain.

[0040] Example: In the battery manufacturing stage, 500 kg of coal is consumed (emission factor 2.45 kg CO2 / kg, emitting 1225 kg CO2), 1000 kWh of electricity is consumed (emission factor 0.8 kg CO2 / kWh, emitting 800 kg CO2), and process emissions are 200 kg CO2e, for a total direct emission of 2225 kg CO2e; implicit emissions, calculated using a matrix, are 500 kg CO2e, resulting in a total carbon footprint of 2725 kg CO2e. This process covers the entire supply chain, ensuring the comprehensiveness and accuracy of the accounting results.

[0041] In some embodiments, based on the LIB-CCGE model, carbon emissions at different stages of the lithium battery supply chain are calculated, including direct emissions, indirect emissions, and implicit emissions.

[0042] The calculation formula for direct emissions is as follows: ; in, E direct For direct emissions, EF f,r,s For the emission factor of fuel f in sector r of region, F f,r,s The consumption of primary energy f by sector s in region r. E process,r,s It refers to the process emissions of department s in region r; The formula for calculating the indirect emissions is as follows: ; in, E indirect Indirect emissions, EF g,r,s The emission factor for the purchase of secondary energy by the regional r sector g. G g,r,s The emissions of secondary energy purchased from external sources by the regional r sector; The formula for calculating the implicit emissions is as follows: ; in, E embodied This represents the implicit emission vectors for products from each department. Let A be the Leontief inverse matrix, and let A be the direct consumption coefficient matrix, obtained from the input-output table.

[0043] In some embodiments, after step S40, step S50, model verification and output, is also included.

[0044] Step S50 verifies the model's accuracy using measured data and generates evaluation results. For example, the measured carbon footprint of a certain region in 2023 was 12.8 kg CO2e / kWh, while the LIB-CCGE model estimated it at 12.5 kg CO2e / kWh, with an error of only 2.3%, which is better than the 15% error of the traditional LCA. The output includes carbon footprint data for each stage and region, such as raw material extraction accounting for 30% of total emissions and the utilization stage accounting for 40%, supporting enterprises in optimizing production processes (e.g., reducing energy consumption) or adjusting supply chain layouts (e.g., choosing low-carbon power regions). The verification process confirms the model's high accuracy and reliability by comparing it with actual monitoring values. For example, in a region dominated by hydropower, the model estimated a 20% reduction in carbon footprint, consistent with the measured results, demonstrating its regional adaptability.

[0045] This invention is a method-based invention, not involving circuits or biochemical ratios, but achieving functionality solely through data fusion, model building, and emissions accounting. The LIB-CCGE model consists of a data input layer (multi-source datasets), a computational layer (material / energy model, CGE framework), and an output layer (carbon footprint results), all tightly connected through data flow and mathematical relationships. The process begins with data acquisition, proceeds through life cycle modeling, regional dynamic adjustments, and emissions accounting, ultimately generating assessment results that can guide practical application.

[0046] Therefore, the refined carbon footprint assessment method for the lithium battery supply chain based on the LIB-CCGE model provided in this embodiment of the invention can be implemented based on the following structures (which can be virtual modular structures): 1) Multi-source heterogeneous data integration platform structure: Enterprise production data, industry statistical data, public databases and regional economic data are integrated through ontology mapping method, and data cleaning, standardization and dynamic updates are performed to form a unified input dataset.

[0047] 2) Lifecycle segmentation and material / energy modeling structure: The lithium battery supply chain is segmented into five stages: raw material mining, material processing, battery manufacturing, usage, and recycling and reuse. Material balance equations and energy consumption and carbon emission equations are established for each stage.

[0048] 3) Regional economic dynamics modeling structure: The multi-regional CGE model built on the GTAP database includes production, consumption, trade, energy and emissions modules as well as policy constraint modules. It realizes the modeling of regional economic dynamics through core equations such as production function, consumption function, investment function and trade function.

[0049] 4) Carbon emission accounting structure: Through direct emission calculation formula, indirect emission calculation formula and implicit emission calculation formula, the carbon footprint of the entire lithium battery supply chain is accurately calculated.

[0050] 5) Model Validation and Output Structure: The model accuracy is verified by comparing it with measured data, and carbon footprint data for each stage and region are generated to provide a scientific basis for enterprise supply chain optimization and government policy formulation.

[0051] The above-mentioned structural combination forms the overall framework of the LIB-CCGE model of this invention, which can realize dynamic assessment, regional adaptability assessment and high-precision accounting of the carbon footprint of the lithium battery supply chain. This is the core technological innovation that distinguishes it from the traditional LCA and static IO models.

[0052] This invention also provides a device for refined assessment of the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model, such as... Figure 2 As shown, the device includes: The data acquisition module 201 is configured to acquire multi-source data and merge the multi-source data into multi-source heterogeneous data; wherein, the multi-source data includes enterprise production data, industry statistics data, public databases and regional economic data related to the lithium battery supply chain; The stage modeling module 202 is configured to divide the lithium battery supply chain into multiple stages and establish material balance equations and energy consumption and carbon emission equations for each stage. The model building module 203 is configured to build a LIB-CCGE model based on the multi-source heterogeneous data; wherein the LIB-CCGE model is used to simulate the impact of energy structure, production technology efficiency and / or environmental policies on carbon footprint in different regions; The carbon emission calculation module 204 is configured to calculate the carbon emissions of the lithium battery supply chain at different stages based on the LIB-CCGE model.

[0053] In some embodiments, the enterprise production data includes energy consumption for battery manufacturing and energy consumption per unit output of cathode material LiCoO2; the industry statistics include global lithium production; the public database includes IEA energy emission factors and coal power; and the regional economic data includes the proportion of thermal power in the region.

[0054] In some embodiments, the data acquisition module is further configured to fuse the multi-source data into multi-source heterogeneous data using the following formula: ; in, For the merged dataset, For the data from the i-th data source, As a data fusion function, it adopts ontology-based mapping rules and reasoning mechanisms to clean, standardize and preprocess the collected raw data, including removing duplicate and erroneous data, handling missing and outlier values, converting data of different dimensions and units into a unified format, and storing the processed data in the database.

[0055] In some embodiments, the stage modeling module is further configured to divide the lithium battery supply chain into five stages: raw material mining, material processing, battery manufacturing, usage, and recycling and reuse.

[0056] In some embodiments, the material balance equation is expressed as: M in,r,s = M out, r,s + M loss, r,s； Wherein, Min,r,s represents the material input of department s in region r, Mout,r,s represents the material output of department s in region r, and Mloss,r,s represents the material loss of department s in region r. The energy consumption and carbon emission equation is expressed as follows: ; ; ; in, It is the greenhouse gas em emitted by sector s in region r. It is the emission factor of greenhouse gas em from fuel f in sector s of region r. It represents the amount of fuel f consumed by department s in region r. Em is the conversion factor for converting a unit of greenhouse gas em to a carbon dioxide equivalent with uniform dimensions. EmTot is the total greenhouse gas emissions from sector s in region r using fuel f after dimension unification. EF f,r,s The greenhouse gas normalized emission factor for fuel f in sector r of region r.

[0057] In some embodiments, the core equations of the LIB-CCGE model include: The production function is expressed as: ; in, For the total output of sector s in region r, Generate function scaling parameters for CES. and These represent the capital and labor factor inputs of sector s in region r, respectively. This represents the production factor ratio parameter of the CES production function. Represents the elasticity of substitution coefficient for the CES production function; The consumption function is expressed as: ; in, For the consumption of products by sector s in region r, It's a consumer preference. It represents the disposable income of region r; The investment function is expressed as: ; in, For investment in regional products, It is an investment tendency. It is the total capital stock of region r; The trade function is expressed as: ; in, This represents the quantity of products from sector s imported by region r from region t. Indicates the trade share parameter. This represents the production price of product in department s of region t. This represents the price of products from sector s imported from region t by region r. Indicates the elasticity of substitution. This represents the total demand of region r for products from department s.

[0058] In some embodiments, based on the LIB-CCGE model, carbon emissions at different stages of the lithium battery supply chain are calculated, including direct emissions, indirect emissions, and implicit emissions.

[0059] In some embodiments, the calculation formula for direct emissions is: ; in, E direct For direct emissions, EF f,r,s For the emission factor of fuel f in sector r of region, F f,r,s The consumption of primary energy f by sector s in region r. E process,r,s It refers to the process emissions of department s in region r; The formula for calculating the indirect emissions is as follows: ; in, E indirect Indirect emissions, EF g,r,s The emission factor for the purchase of secondary energy by the regional r sector g. G g,r,s The emissions of secondary energy purchased from external sources by the regional r sector; The formula for calculating the implicit emissions is as follows: ; in, E embodied This represents the implicit emission vectors for products from each department. Let A be the Leontief inverse matrix, and let A be the direct consumption coefficient matrix, obtained from the input-output table.

[0060] It should be noted that the various device structures described in this embodiment and the previously described methods belong to the same technical concept and achieve the same technical effect through the same principle, which will not be repeated here.

[0061] This invention also provides a readable storage medium storing one or more programs that can be executed by one or more processors to implement the methods described in any of the above embodiments.

[0062] Furthermore, although exemplary embodiments have been described herein, their scope includes any and all embodiments based on the invention that have equivalent elements, modifications, omissions, combinations (e.g., schemes involving intersections of various embodiments), adaptations, or alterations. Elements in the claims will be interpreted broadly based on the language used in the claims and are not limited to the examples described in this specification or during the implementation of this application, and such examples will be interpreted as non-exclusive. Therefore, this specification and examples are intended to be considered illustrative only, and the true scope and spirit are indicated by the following claims and the full scope of their equivalents.

[0063] The above description is intended to be illustrative and not restrictive. For example, the above examples (or one or more of them) can be used in combination with each other. Other embodiments may be used by those skilled in the art upon reading the above description. Furthermore, in the above detailed description, various features may be grouped together to simplify the invention. This should not be construed as an intention that a feature of an unclaimed invention is necessary for any claim. Rather, the subject matter of the invention may be less than all the features of a particular embodiment of the invention. Thus, the following claims are incorporated herein by reference as examples or embodiments, wherein each claim is independently considered as a separate embodiment, and these embodiments are contemplated as being able to be combined with each other in various combinations or arrangements. The scope of the invention should be determined by reference to the appended claims and the full scope of their equivalents.

Claims

1. A refined assessment method for the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model, characterized in that, The method includes: Acquire multi-source data and merge the multi-source data into multi-source heterogeneous data; wherein, the multi-source data includes enterprise production data, industry statistics data, public databases and regional economic data related to the lithium battery supply chain; The lithium battery supply chain is divided into multiple stages, and material balance equations and energy consumption and carbon emission equations are established for each stage. Based on the aforementioned multi-source heterogeneous data, a LIB-CCGE model is established; wherein, the LIB-CCGE model is used to simulate the impact of energy structure, production technology efficiency and / or environmental policies on carbon footprint in different regions; Based on the LIB-CCGE model, carbon emissions at different stages of the lithium battery supply chain are calculated. The material balance equation is expressed as follows: M in,r,s = M out, r,s + M loss, r,s in, M in,r,s This represents the material input quantity for department s within region r. M out,r,s This represents the material output of department s in region r. M loss,r,s This represents the material loss amount in department s within region r; The energy consumption and carbon emission equation is expressed as follows: in, It is the greenhouse gas em emitted by sector s in region r. It is the emission coefficient of greenhouse gas em from fuel f in sector s of region r. It represents the amount of fuel f consumed by department s in region r. Em is the conversion factor for converting a unit of greenhouse gas em to a carbon dioxide equivalent with uniform dimensions. EmTot is the total greenhouse gas emissions from sector s using fuel f in region r after dimension unification. EF f,r,s The normalized greenhouse gas emission factor for fuel f in sector r of region; The core equations of the LIB-CCGE model include: The production function is expressed as: in, For the total output of sector s in region r, Generate function scaling parameters for CES. and These represent the capital and labor factor inputs of sector s in region r, respectively. This represents the production factor ratio parameter of the CES production function. Represents the elasticity of substitution coefficient for the CES production function; The consumption function is expressed as: in, For the consumption of products by sector s in region r, It's a consumer preference. It represents the disposable income of region r; The investment function is expressed as: in, For investment in regional products, It is an investment tendency. It is the total capital stock of region r; The trade function is expressed as: in, This represents the quantity of products from sector s imported by region r from region t. Indicates the trade share parameter. This represents the production price of product s in department s of region t. This represents the price of products from sector s imported from region t by region r. Indicates the flexibility of substitution. This represents the total demand of region r for products from department s.

2. The method according to claim 1, characterized in that, The enterprise production data includes energy consumption in battery manufacturing and energy consumption per unit output of cathode material LiCoO2; the industry statistics include global lithium production; the public databases include IEA energy emission factors and coal-fired power; and the regional economic data includes the proportion of thermal power in the region.

3. The method according to claim 1, characterized in that, The multi-source data is fused into multi-source heterogeneous data using the following formula: in, For the merged dataset, For the data from the i-th data source, As a data fusion function, it adopts ontology-based mapping rules and reasoning mechanisms to clean, standardize and preprocess the collected raw data, including removing duplicate and erroneous data, handling missing and outlier values, converting data of different dimensions and units into a unified format, and storing the processed data in the database.

4. The method according to claim 1, characterized in that, The lithium battery supply chain is divided into five stages: raw material mining, material processing, battery manufacturing, usage, and recycling and reuse. These stages are represented in the model as sectors.

5. The method according to claim 1, characterized in that, Based on the LIB-CCGE model, carbon emissions at different stages of the lithium battery supply chain are calculated, including direct emissions, indirect emissions, and implicit emissions.

6. The method according to claim 5, characterized in that, The calculation formula for direct emissions is as follows: in, E direct For direct emissions, EF f,r,s For the emission factor of fuel f in sector r of region, F f,r,s The consumption of primary energy f by sector s in region r. E process,r,s It refers to the process emissions of department s in region r; The formula for calculating the indirect emissions is as follows: in, E indirect Indirect emissions, EF g,r,s The emission factor for the purchase of secondary energy by the regional r sector g. G g,r,s The emissions of secondary energy purchased from external sources by the regional r sector; The formula for calculating the implicit emissions is as follows: in, E embodied Let L = (I - A) be the implicit emission vector for each product segment. -1 Let A be the Leontief inverse matrix, and let A be the direct consumption coefficient matrix, obtained from the input-output table.

7. A refined assessment device for the carbon footprint of the lithium battery supply chain based on the LIB-CCGE model, used to implement the method as described in any one of claims 1 to 6, characterized in that, The device includes: The data acquisition module is configured to acquire multi-source data and merge the multi-source data into multi-source heterogeneous data; wherein, the multi-source data includes enterprise production data, industry statistics data, public databases and regional economic data related to the lithium battery supply chain; The phase modeling module is configured to divide the lithium battery supply chain into multiple phases and establish material balance equations and energy consumption and carbon emission equations for each phase. The model building module is configured to build a LIB-CCGE model based on the multi-source heterogeneous data; wherein the LIB-CCGE model is used to simulate the impact of energy structure, production technology efficiency and / or environmental policies on carbon footprint in different regions; The carbon emission calculation module is configured to calculate the carbon emissions of the lithium battery supply chain at different stages based on the LIB-CCGE model.

8. A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, perform the method according to any one of claims 1 to 6.

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